Case Study on Mobile Virtual Reality Construction Training
Bibliographic record
Abstract
Case Study on Mobile Virtual Reality Construction Training Mario Wolf, Jochen Teizer and J.H. Ruse Pages 1231-1237 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Recent surveys among construction firms found, a majority has a hard time filling craft worker/hourly positions and salaried jobs. Among the ways they are trying to create more is in-house training. However, existing learning methods have been lagging effectiveness or are outdated. New approaches, like mobile virtual reality, are being investigated. In this paper, the authors describe their approach to a low cost virtual reality training that offers personalized feedback for trainees or workers. The developed approach utilizes elements of gamification for motivational purposes. While the training requirements were gathered in dialogue with leading companies in the construction and engineering industry sectors, the research conducted focused on prototyping and testing the novel learning concept. As a result, the authors developed a mobile virtual reality application that utilizes the Google Daydream SDK that runs on Google Cardboard, Samsung Gear VR, Oculus Go or compatible other inexpensive devices. The application was tested and evaluated by industry representatives. An outlook provides the path forward in research and development. Keywords: digitalization; construction safety; personalized feedback; virtual reality; virtual trainings; workforce education and training DOI: https://doi.org/10.22260/ISARC2019/0165 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".